Accelerated Bayesian inference-based history matching of petroleum reservoirs using polynomial chaos expansions

Khatoon, Sufia; Phirani, Jyoti; Bahga, Supreet Singh

doi:10.1080/17415977.2021.1973455

Cited by 4 publications

(2 citation statements)

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“…These reservoir properties are estimated with the support of seismic inversion by geologists . However, the technique has significant uncertainties in the estimations of the reservoir scale flow properties, particularly far from the wells. , …”

Section: Introductionmentioning

confidence: 99%

“…20 However, the technique has significant uncertainties in the estimations of the reservoir scale flow properties, particularly far from the wells. 21,22 Another extensively employed method for geophysical monitoring of reservoirs involves time-lapse seismic monitoring, where multiple seismic surveys are acquired and analyzed over time at the same site. 23 The initial survey conducted in the field is referred to as the baseline survey, while all subsequent surveys are termed monitor surveys.…”

Section: Introductionmentioning

confidence: 99%

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Uncertainty Propagation in Predicting Recovery from Gas Hydrate Reservoirs: From Reservoir Characteristics to Performance Analysis

Choudhary,

Phirani

2024

Energy Fuels

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The necessity of forecasting recovery from the gas hydrate reservoirs is restricted due to the limiting data availability for reservoir properties, such as porosity and hydrate saturation, used in reservoir models. Predicting recovery becomes essential to determine the feasibility and viability of a reservoir. Therefore, it is paramount to understand uncertainty propagation from reservoir characteristics to reservoir performance by using reservoir simulations. In this work, we explore the uncertainty in reservoir performance due to the uncertainty in reservoir characterization of porosity and saturation. We employ the Monte Carlo method to analyze the impact of uncertainty on gas and water production from a class 2, confined, oceanic methane hydrate reservoirs. We observe that uncertainty in porosity leads to a substantial uncertainty in our prediction of gas recovery until a critical porosity value. If the porosity is higher than the critical porosity value, then the gas production using depressurization does not change significantly. This is attributable to the thermal nature of gas hydrate dissociation in which high porosity means less heat content from the sediments for hydrate dissociation. For the same reason, at the same porosity, variation in hydrate saturation does not lead to a high variation in gas recovery. The uncertainty in porosity leads to a 24% coefficient of variation in cumulative gas production, uncertainty in initial hydrate saturation leads to 3% and their combined uncertainty leads to 20% coefficient of variation in cumulative gas production. Using the cumulative density function of the gas production, we show that our P 90 , P 50 , and P 10 estimates will vary depending on the production time considered for estimation.

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Section: Introductionmentioning

confidence: 99%